A SHORT INTRODUCTION TO SOME RECENT PROGRESS IN PHYLOGENETIC NETWORK RECONSTRUCTION, GENOME MAPPING, GENE EXPRESSION ANALYSIS, MOLECULAR DYNAMIC SIMULATION, AND OTHER PROBLEMS IN BIOINFORMATICS LIMSOON WONG Managing EditorPhylogenetic network is a way to describe evolutionary histories that have under- gone evolutionary events such as recombination, hybridization, or horizontal gene transfer. The level, k, of a network determines how non-treelike the evolution can be, with level-0 networks being trees. A number of methods for constructing rooted phylogenetic network from triplets have been proposed in the past. 1,2 In this issue, Gambette et al. 3 discuss how to generalize these methods to construct unrooted phylogenetic network from quartets. The paper has three main contributions: (1) it gives an Oðn 5 ð1 þ ðn; nÞÞÞ time algorithm to compute the set of quartets of a network; (2) it shows that level-1 quartet consistency is NP-hard; and (3) given a set Q of quartets, it shows that Oðn 4 Þ time is su±cient to compute the unrooted level-1 network Nsuch that Q ¼ QðNÞ, if it exists. Modern DNA sequencers produce an explosive amount of sequence data of rela- tively short read lengths. A number of fast genome mapping tools, which use the BurrowsÀWheeler transforms 4 for seed search and dynamic programming for ex- tension, have been developed. Myers proposed an elegant dynamic programming method for this problem that uses bit-parallelism for approximate string matching. However, it comes with a restriction that the query length should be within the word size of the computer. In this issue, Kimura et al. 5 propose a modi¯cation of Myers' algorithm that removes the restriction on the query length. Gene expression analysis is a powerful way to detect the biological signature of a disease. 6À8 In this issue, Han and Dong 9 introduce new ideas to optimize the diversity of decision trees in an ensemble classi¯er, CABD, for gene expression pro¯le classi- ¯cation. CABD is shown to be superior to other ensemble methods. Moreover, the diversi¯ed features produced by CABD are also useful for improving the performance of other classi¯ers, e.g. SVM. In another paper in this issue, Xu 10 describes an approach to identify di®erentially expressed genes in non-homogeneous time course Journal of Bioinformatics and Computational Biology Vol. 10, No. 4 (2012) 1203002 (3 pages) # . c Imperial College Press DOI: 10.1142/S0219720012030023 J . B i o i n f o r m . C o m p u t . B i o l . 2 0 1 2 . 1 0 . D o w n l o a d e d f r o m w w w . w o r l d s c i e n t i f i c . c o m b y 8 5 . 7 4 . 8 4 . 1 3 4 o n 1 0 / 2 3 / 1 2 . F o r p e r s o n a l u s e o n l y .
